Terrain Analysis Using Radar Shape-from-Shading
IEEE Transactions on Pattern Analysis and Machine Intelligence
A differential evolution based incremental training method for RBF networks
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Prediction of mean monthly total ozone time series-application of radial basis function network
International Journal of Remote Sensing
Efficient Training of RBF Networks Via the BYY Automated Model Selection Learning Algorithms
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
MAD Loss in Pattern Recognition and RBF Learning
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Diagnosis of Cervical Cancer Using the Median M-Type Radial Basis Function (MMRBF) Neural Network
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Fuzzy model identification using support vector clustering method
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Building RBF neural network topology through potential functions
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Radial basis function neural network based on order statistics
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Median M-type radial basis function neural network
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Comparison of neural classifiers for vehicles gear estimation
Applied Soft Computing
A hierarchical procedure for the synthesis of ANFIS networks
Advances in Fuzzy Systems
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Radial basis functions (RBFs) consist of a two-layer neural network, where each hidden unit implements a kernel function. Each kernel is associated with an activation region from the input space and its output is fed to an output unit. In order to find the parameters of a neural network which embeds this structure we take into consideration two different statistical approaches. The first approach uses classical estimation in the learning stage and it is based on the learning vector quantization algorithm and its second-order statistics extension. After the presentation of this approach, we introduce the median radial basis function (MRBF) algorithm based on robust estimation of the hidden unit parameters. The proposed algorithm employs the marginal median for kernel location estimation and the median of the absolute deviations for the scale parameter estimation. A histogram-based fast implementation is provided for the MRBF algorithm. The theoretical performance of the two training algorithms is comparatively evaluated when estimating the network weights. The network is applied in pattern classification problems and in optical flow segmentation